Learning Knowledge from Relevant Webpage for Opinion Analysis

This paper presents an opinion analysis system based on linguistic knowledge which is acquired from small-scale annotated text and raw topic-relevant Web page. Based on the observation on the annotated opinion corpus, some word-, collocation- and sentence-level linguistic features for opinion analysis are discovered. Supervised and unsupervised learning techniques are developed to learn these features from annotated text and raw relevant Web page, respectively. These features are then incorporated into a classifier based on support vector machine (SVM) to identify opinionated sentences and determine their polarities. Evaluations show that the proposed opinion analysis system, namely OA, achieved promising performance, which shows the effectiveness of linguistic knowledge learning from relevant Web page.